SolidWaste presentation 080408 final · Hurricane Simulation and Debris Assessment: “Katrina”...
Transcript of SolidWaste presentation 080408 final · Hurricane Simulation and Debris Assessment: “Katrina”...
Hurricane DebrisHurricane DebrisManagement
Workshop
Sponsored by:
University of Florida Hinkley Center for Solid and Hazardous Waste
Alachua County Solid Waste and Emergency Management
April 9, 2008
g
Agenda
9:45‐10:00 Refreshments
10:00‐10:30 Introduction and Opening Remarks
John Schert, DirectorHinkley Center for Solid and Hazardous Waste, University of Florida
K D t A i t t P bli W k Di tKaren Deeter, Assistant Public Works DirectorAlachua County Waste Management
David Donnelly, DirectorAl h C E MAlachua County Emergency Management
Charles Kibert, Ph.D., P.E., Principal InvestigatorM.E. Rinker Sr., School of Building Construction, University of Florida
10:30‐11:30 HAZUS‐MH: Part I: Hurricane Simulation and Debris Assessment:“Charley, Frances and Ivan”
K R Grosskopf Ph D and Eric Kramer Ph DK.R. Grosskopf, Ph.D. and Eric Kramer, Ph.D.M.E. Rinker Sr., School of Building Construction, University of Florida
11:30‐12:00 Lunch (provided) 2
Agenda
12:00‐12:30 HAZUS‐MH: Part IIHurricane Simulation and Debris Assessment: “Katrina”
K.R. Grosskopf, Ph.D. and Eric Kramer, Ph.D.M.E. Rinker Sr., School of Building Construction, University of Florida
12:30‐1:15 Applying for FEMA Disaster Assistance: Eligible and Non‐Eligible Debris
Phil W. Worley, State Debris Coordinator Florida Division of Emergency Management (FDEM)
1:15‐1:30 Break
1:30‐2:15 Special Discussion Topics
Karen Deeter Assistant P blic Works DirectorKaren Deeter, Assistant Public Works DirectorAlachua County Waste Management
David Donnelly, DirectorAl h C t E M tAlachua County Emergency Management
2:15‐2:30 Closing Remarks3
HAZUS‐MH: Hurricane Simulation andHAZUS MH: Hurricane Simulation and Debris Assessment
K.R. Grosskopf, Ph.D and Eric W. Kramer, Ph.D
M.E. Rinker Sr., School of Building Construction
U i it f Fl idUniversity of Florida
April 9, 2008
Introduction
►2004 storms generated ~ 75M yd3 of C&D and vegetative debris in six weeks, as much as would g ,normally be seen statewide in three years
►~350 major staging areas; 4,000 acres
►Debris• Clean waste: recycled, energy generation
• Commingled (mixed) waste: burned, landfill
►“Storm chasers”• Out‐of‐state workforce
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Overview
►What is HAZUS‐MH?
►H i Si l ti d D b i A t PART I►Hurricane Simulation and Debris Assessment, PART I• Charley, Frances & Ivan (2004)
►Hurricane Simulation and Debris Assessment, PART II• Category 5 “Katrina” scenario
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Overview
60%
70%
80%
90%
100%
Tota
l Uni
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Urban
R2 = 0.9765
0%
10%
20%
30%
40%
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60%
Bui
ldin
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amag
e (%
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20602005
UrbanConservation
0%50 60 70 80 90 100 110 120 130 140 150
Peak Windspeed (mph)
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HAZUS‐MH: A Decision Support Tool for Disaster Preparedness and Emergency Management
►HAZUS – Natural hazards loss estimation tool in GISCharacterized by:Characterized by:
• Geographically sensitive, risk‐based approach
• Validated scientific methodology to estimate damages• Validated scientific methodology to estimate damages, losses, and mitigation benefits
• Interactive visual display and identification of hazards and p yvulnerability
• Assistance with decision‐making and comprehensive h h dapproach to hazards
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HAZUS‐MH Data
►Over 200 default layers• General Inventory
• Building profiles and occupancy
• Essential facilities
• Demographics
►Hazard Specific Inventory• Terrain, elevation, storm tracks
►User supplied data• Updated building inventory, local critical facilitiesUpdated building inventory, local critical facilities
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HAZUS scenario outputs
►Outputs include maps, reports, and tabular data
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HAZUS wind model methodology
►“Hazard‐load‐resistance‐damage‐loss” methodology
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HAZUS limitations
►HAZUS at present cannot model:• Storm surges, wave damage, or wind damage to transportation, utility Sto su ges, a e da age, o d da age to t a spo tat o , ut ty
networks, tree damage to buildings
►Wind and Flood models are independent• No ability to model floods caused by hurricane rains or flooding by
urban storm water runoff; Potential for duplication of damage/losses
► Low intensity storms and winds are difficult to model► Low intensity storms and winds are difficult to model• Hurricanes Frances and Jeanne in Alachua County
►HAZUS does not account for events that precede the scenario►HAZUS does not account for events that precede the scenario• Soil saturation, sequential storm strikes, etc.
►Scale and accuracy►Scale and accuracy• Regional scale scenarios give most accurate damage and loss estimates
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Examples of HAZUS scenarios
►2007 Alachua and Nassau County Hurricane Simulation and Damage AssessmentSimulation and Damage Assessment• 5 scenarios, including:
− 2004 storms2004 storms
− Historical 1896, 1898 storms
− Worst case event
− Probabilistic storms
− 100 year flood event
►Scenario development• Storm parameters
− NWS assistance to review and adjust deterministic scenarios
• NOAA H*Wind post‐storm analyses 13
HAZUS scenario outputs
►Wind speeds• Peak and max sustained windsPeak and max sustained winds
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HAZUS scenario outputs
►Wind speeds• Peak and max sustained windsPeak and max sustained winds
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HAZUS scenario outputs
►Building damage and economic losses
Severe damage:Failure of>50% roof cover>50% roof cover>20% window 10‐20 impacts
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HAZUS scenario outputs
►Building damage and economic losses
Residential Loss
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HAZUS scenario outputs
►Essential facility damage
Schools
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HAZUS scenario outputs
►Shelter requirements
DisplacedHouseholds
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HAZUS scenario outputs
►Debris
Brick WoodOther
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HAZUS scenario outputs
►Reports
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HAZUS scenario outputs
►Reports
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HAZUS scenario outputs
►Flood model• Digital• Digital
ElevationModel
Inflow / outflow conditions affectscenarios
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HAZUS scenario outputs
►Floodmodel• 100 Year• 100 Year
event
Direct damageInduced damageDirect losses**I di l**Indirect losses
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HAZUS scenario outputs
►Flood model• Bridge• Bridge
Damage
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HAZUS scenario outputs
►Flood model• Total• Total
EconomicLoss
Nassau CountyTotal losses: $440 Million33% Business Interruption50% Residential
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HAZUS scenario outputs
►Flood model• Residential• Residential
EconomicLoss
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HAZUS scenario outputs
►Flood model• Debris• Debris
Total
152,000 tons38% Finishes29% Structures33% Foundations
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Hurricane Simulation and Debris Assessment, PART I
Charley, Frances & Ivan (2004)
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Hurricane Debris Modeling for 2004 Storms
►Scenarios to model debris for 2004 hurricanes:• Charley• Charley
• Frances
• Ivan• Ivan
►Impact on 5 urban coastaland rural inland countiesand rural inland counties• Charlotte and De Soto
• St Lucie and Okeechobee• St. Lucie and Okeechobee
• Escambia
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Hurricane Debris Study: Validation
►Actual debris data (2004 storms)• Difficulty in obtaining accurate data• Difficulty in obtaining accurate data
− Counties impacted by multiple storms; clean‐up not complete before next storm strike
− Cross‐county staging areas, regional transfer stations, etc.
− Private contractors
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Hurricane Debris Study
►HAZUS Debris Estimates• Census tract level• Census tract level
• Building inventory and vegetation
• Storm hazards (wind vs flood) may affect results• Storm hazards (wind vs. flood) may affect results
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Hurricane Charley scenario: Charlotte County
►Storm parameters• NOAA H*wind post storm analyses• NOAA H*wind post‐storm analyses
Landfall
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Hurricane Charley scenario: Charlotte County
►Winds
Gusts>125 mph
PORT CHARLOTTE PUNTA GORDA
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Hurricane Charley scenario: Charlotte County
►Winds
Max sustained>100 mph>100 mph
PORT CHARLOTTE PUNTA GORDA
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Hurricane Charley scenario: Charlotte County
►Damage and loss estimates
48% 11%5%
78%
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Hurricane Charley scenario: Charlotte County
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled b
PORT CHARLOTTE PUNTA GORDA
tree debris
• Total tree debristree debris
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Hurricane Charley scenario: Charlotte County
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled b
PORT CHARLOTTE PUNTA GORDA
tree debris
• Total tree debristree debris
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Hurricane Charley scenario: Charlotte County
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled b
PORT CHARLOTTE PUNTA GORDA
tree debris
• Total tree debristree debris
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Hurricane Charley scenario: Charlotte County
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled b
PORT CHARLOTTE PUNTA GORDA
tree debris
• Total tree debristree debris
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Comparative debris summary, 2004 storms
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Estimated Debris from 2004 Storms
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Estimated Debris from 2004 Storms
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Estimated Debris from 2004 Storms
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Estimated Debris from 2004 Storms
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Estimated Debris from 2004 Storms
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Estimated Debris from 2004 Storms
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Estimated Debris from 2004 Storms
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►Session Break
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Hurricane Simulation and Debris Assessment, PART II
Category 5 “Katrina” Scenario
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Part II: HAZUS‐MH Hurricane Simulation and Debris Assessment: Worst case “Katrina” storm
►Model the impacts that a hypothetical category 5 storm would have on the five counties
►Each scenario builds on the 2004 storm tracks, ramped up to strong category 5, a "worst case" scenario
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Worst case category 5 scenario: Escambia
►Storm parameters• Storm path remains same as Ivan• Storm path remains same as Ivan
• Assistance from NWS meteorologists
Landfall
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Worst case category 5 scenario: Escambia
►Winds
Gusts > 160 mph
PENSACOLA
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Worst case category 5 scenario: Escambia
►Winds
MaxSustained>127 mph>127 mph
PENSACOLA
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Worst case category 5 scenario: Escambia
►Damage and loss estimates
62%
71%
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Worst case category 5 scenario: Escambia
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete
15x
and steel
• Eligibled btree debris
• Total tree debristree debris
PENSACOLAPENSACOLA
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Worst case category 5 scenario: Escambia
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled b
100x
tree debris
• Total tree debristree debris
PENSACOLAPENSACOLA
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Worst case category 5 scenario: Escambia
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled btree debris
• Total tree debris
3x
tree debris
PENSACOLAPENSACOLA
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Worst case category 5 scenario: Escambia
►Debris estimates• Brick wood• Brick, wood,
and other
• Concrete and steel
• Eligibled btree debris
• Total tree debristree debris
PENSACOLAPENSACOLA
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Estimated Debris ‐Worst Case Cat 5 Storms
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Estimated Debris ‐Worst Case Cat 5 Storms
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Estimated Debris ‐Worst Case Cat 5 Storms
St. Lucie
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Worst case: 300x Frances
Estimated Debris ‐Worst Case Cat 5 Storms
St. Lucie
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Worst case: >30x Frances
Estimated Debris from Worst Case Cat 5
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Estimated Debris ‐Worst Case Cat 5 Storms
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Estimated Debris ‐Worst Case Cat 5 Storms
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Estimated Debris ‐Worst Case Cat 5 Storms
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Trend Analyses and Future Activities
60%
70%
80%
90%
100%
Tota
l Uni
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Urban
R2 = 0.9765
0%
10%
20%
30%
40%
50%
60%
Bui
ldin
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amag
e (%
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20602005
UrbanConservation
0%50 60 70 80 90 100 110 120 130 140 150
Peak Windspeed (mph)
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Trend Analyses
►Trend analyses were prepared to determine the extent of damage to buildings and debris generationextent of damage to buildings and debris generation associated with intensity of peak and sustained wind speeds.
►Results indicate that little building damage occurs until peak wind speeds exceed approximately 90 mph. p p pp y pBeyond this point, the increase in damage and building debris generation increases exponentially with respect to increases in peak wind speed.
►However, significant damage to buildings from fallen trees and, significant vegetative debris generation is likely at sub‐hurricane wind speed 69
Trend Analyses – Building Damage
80%
90%
100%
nits
)
R2 = 0.946450%
60%
70%
mag
e (%
Tot
al U
n
10%
20%
30%
40%
Bui
ldin
g D
am
0%
10%
60 80 100 120 140 160 180 200
Peak Windspeed (mph)
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Trend Analyses – Building Damage
80%
90%
100%
nits
)
R2 = 0.976550%
60%
70%
mag
e (%
Tot
al U
n
10%
20%
30%
40%
Bui
ldin
g D
am
0%
10%
50 60 70 80 90 100 110 120 130 140 150
Peak Windspeed (mph)
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Trend Analyses – Building Damage
80.0%
90.0%
100.0%
ts)
50.0%
60.0%
70.0%
ge (%
Tot
al U
nit
Destroyed
Severe
Moderate
20.0%
30.0%
40.0%
Bui
ldin
g D
amag Moderate
Minor
0.0%
10.0%
CAT <1 CAT 1 CAT 2 CAT 3 CAT 4
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Trend Analyses – Debris
R2 = 0.78755,000,000
6,000,000
7,000,000
ons) R2 = 0.6672
50
60
70
pita
(ton
s)
1,000,000
2,000,000
3,000,000
4,000,000
Tota
l Deb
ris (t
10
20
30
40
Tota
l Deb
ris p
er C
ap
060 70 80 90 100 110 120 130 140 150 160
Max Sustained Windspeed (mph)
060 80 100 120 140 160 180 200
Peak Windspeed (mph)
Wind Speed Population
R2 = 0.6545120
140
160
180
ldin
g (t
ons)
R2 = 0.80495 000
6,000
7,000
8,000
9,000
e M
ile (t
ons)
20
40
60
80
100
Tota
l Deb
ris p
er B
ui
1,000
2,000
3,000
4,000
5,000To
tal D
ebris
per
Squ
are
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060 80 100 120 140 160 180 200
Peak Windspeed (mph)
Land AreaBuildings
-1,000
060 70 80 90 100 110 120 130 140 150 160
Max Sustained Windspeed (mph)
Trend Analyses – Debris
40,000
45,000
50,000
ity
R2 = 0.9265
25,000
30,000
35,000
per U
rban
Den
sap
ita/s
q m
ile)
5 000
10,000
15,000
20,000
Tota
l Deb
ris
(ton
s/ca
0
5,000
60 80 100 120 140 160 180 200
Peak Windspeed (mph)
74
(1σ)
Trend Analyses – Debris
40,000
45,000
50,000
sity
R2 = 0.9264
25,000
30,000
35,000
per U
rban
Den
sap
ita/s
q m
ile)
5 000
10,000
15,000
20,000
Tota
l Deb
ris
(ton
s/c a
0
5,000
60 70 80 90 100 110 120 130 140 150 160
Max Sustained Windspeed (mph)
75
(1σ)
Trend Analyses – Debris
3,500,000
4,000,000
2,000,000
2,500,000
3,000,000
s (t
ons)
Tree (ineligible)
Tree (eligible)
1,000,000
1,500,000Deb
ris
Building
0
500,000
CAT <1 CAT 1 CAT 2 CAT 3 CAT 4
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Trend Analyses ‐ Debris
►Hurricane Debris Estimating Model
• Developed by the U.S. Army Corps of Engineers
• Contained in the Federal Emergency Management Agency (FEMA) 2003 Debris Management Guide, FEMA Publication 325Debris Management Guide, FEMA Publication 325
• Based on actual data from Hurricanes Frederic, Hugo and Andrew and has a reported accuracy of +/‐ 30%
• Assumes approximately 30% of the debris will be clean woody wastes; remaining 70% assumed to be mixed (C&D) debris
• Of the 70% mixed C&D, 42% is assumed to be burnable after sorting, 5% is soil, 15% are metals and 38% landfill
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Trend Analyses ‐ Debris
►Hurricane Debris Estimating Model
Q = H * C * V * B * S * Z
where: Q = the quantity of hurricane‐generated debris, yd3
H = the estimated number of households impacted
C = a storm category factor, yd3
V = a vegetation characteristic multiplier
B i l/b i /i d t i l lti liB = a commercial/business/industrial use multiplier
S = a storm precipitation characteristic multiplier
Z = percentage of land area impacted
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p g p
Trend Analyses ‐ Debris
Hurricane Category Value of “C” Factor (yd3)
1 21 2
2 8
3 263 26
4 50
5 805 80
Vegetative Cover Value of “V” Multiplierg p
Light 1.1
Medium 1.3
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Heavy 1.5
Trend Analyses ‐ Debris
Commercial Density Value of “B” Multiplier
Li h 1 0Light 1.0
Medium 1.2
Heavy 1 3Heavy 1.3
Precipitation Characteristic Value of “S” MultiplierPrecipitation Characteristic Value of S Multiplier
None to Light 1.0
Medium to Heavy 1.3 y
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Trend Analyses – Debris
1 200 000
1,400,000
1,600,000
800,000
1,000,000
1,200,000
Deb
ris (t
ons) HAZUS
COE
200,000
400,000
600,000
Tota
l
0Charlotte(Charley,
2004)
Escambia(Ivan, 2004)
St. Lucie(Frances,
2004)
DeSoto(Charley,
2004)
Okeechobee(Frances,
2004)
Alachua(Frances,
2004)
Alachua(Jeanne,
2004)
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HAZUS vs Corps of Engineers (FEMA Pub 325)
Trend Analyses – Debris
1,393,9611,519,747
1 200 000
1,400,000
1,600,000
928,515
800,000
1,000,000
1,200,000
Debr
is (t
ons)
200,000
400,000
600,000
Tota
l
0HAZUS COE (FEMA) Actual
Hurricane Ivan, 2004
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Simulation vs Actual
Trend Analyses
►Variance in damage and debris output increases significantly with increase in storm intensitysignificantly with increase in storm intensity
►Ratio of building vs. tree debris increases significantly with increase in storm intensitywith increase in storm intensity
►Slightly higher correlation of building damage and debris to maximum sustained winddebris to maximum sustained wind• Cycle fatigue and progressive failure vs. catastrophic failure
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Future Activities
►Service• HAZUS simulations (state regional and county)• HAZUS simulations (state, regional and county)
− Building damage
− Debris generationg
− Displaced populations and shelter requirements
− Emergency services impacts
− Flooding and storm surge (SLOSH)
− Economic loss
• Alachua, Charlotte, DeSoto, Escambia, Nassau, Okeechobee, St. Lucie
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Future Activities
►Research• Building mitigation• Building mitigation
− Property appraiser data
− Manufactured housingg
• Improvements to 1994 HUD Code and 1999 FAC 15‐C
• 2002 FBC add‐on structures
− Site‐built housing and commercial buildings
• Improvements to 2002 FBC and (Miami‐Dade) PAS
• Roof coverings and sheathing attachmentRoof coverings and sheathing attachment
• Door and window impact resistance
• Infrastructure− Transportation, utilities, etc.
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Future Activities
►Research (con’t)• Landscape designLandscape design
− Satellite tree survey, type and distribution of vulnerable species
• Land use planning− 2020 and 2050 state comp plans and future growth
− Climate change
• Storm intensity and frequency (ASCE‐07 wind zones)• Storm intensity and frequency (ASCE‐07 wind zones)
• Sea level rise
• Injury and fatality simulations− Flood, storm surge, population density, socioeconomics, etc.
− USAR and first response
− Clean‐up and recovery workers− Clean‐up and recovery workers
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HAZUS Resources
►www.HAZUS.org
► HAZUS /SEHUG►www.HAZUS.org/SEHUG
►www.flhug.HAZUS.org
►For more information about the Florida Hurricane Simulation and Damage Assessment project, contact:Simulation and amage Assessment project, contact:• Kevin R. Grosskopf: [email protected]
• Eric W. Kramer: [email protected]
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@
Discussion
►Questions and comments?Q
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References
► FEMA (2007). HAZUS‐MH Hurricane Wind Model Validation Study ‐ Florida. Washington, DC, Federal Emergency Management Agency (FEMA): 1‐131.
► Vickery, P. J., J. Lin, et al. (2006). "HAZUS‐MH Hurricane Model Methodology. I: Hurricane Hazard, Terrain, and Wind Load Modeling." Natural Hazards Review 7(2): 82‐93.
► Vickery, P. J., P. F. Skerlj, et al. (2006). "HAZUS‐MH Hurricane Model Methodology. II: Damage and Loss Estimation." Natural Hazards Review 7: 94.9 .
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